Continuing Evolution of Weather Radar Technology

Masakazu Wada

Chief Specialist
Defense & Electronic Systems Division
Toshiba Infrastructure Syastems & Solutions Corporation

1. Introduction
Occurrence of damage due to typhoons, linear-precipitation
zones and localized heavy rain has increased recently, and becomea new social issue. Weather radar can be effective for observing
these sorts of precipitation, spatially and temporally over wide
areas, both quantitatively and at fine intervals. Weather radar isoperated by various agencies including the Meteorological Agency,
the Water and Disaster Management Bureau of the Ministry ofLand, Infrastructure, Transport and Tourism (MLIT), and local
governments, and is used for purposes such as managing rivers,
drainage and other social infrastructure, for weather forecasts,
and to provide information to residents. Japan’s weather radar
technology is advanced, leading the industry with features suchas solid-state transmitters and high-speed observations throughelectronic scanning. This article gives an overview of weather
radar concepts and the development of weather radar technology.

2. Precipitation observations using weatherradar
Rain gauges have long been used to understand precipitation,and the Automated Meteorological Data Acquisition System(AMeDAS) operated by the Meteorological Agency takes
precipitation measurements, at approximately 840 locations
throughout Japan at roughly 21 km intervals, using tipping-bucket
rain gauges. Tipping-bucket rain gauges collect and directly
measure precipitation so they are very reliable, but they have issues
including: (1) they are only able to measure precipitation at thedevice location, (2) accuracy of instantaneous measurements suchas volume/minute is poor, and (3) precipitation amounts can beunderestimated if there is cross wind.
In contrast, weather radar observations have advantages
including (1) wide area observations ranging from tens tohundreds of km in radius, (2) instantaneous estimates of rainfall,minute-to-minute, and (3) ability to make observations ataltitude, and not just at the ground level. In the past, precipitation
estimations were not always adequate due to lack of precision andpoor maintainability, but these limitations are being removed
through new technologies such as dual-polarization observations
and solid-state transmitters.
As shown in Figure 1, weather radar transmits radio signals
into the air and then the antenna receives the back-scatter signalthat occurs when the transmitted signal hits various precipitation
particles (rain, snow, hail, etc.) suspended in the air, enabling
estimation of the state of precipitation in the air (quantity,movement, particle type, etc.).

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■ Figure 1: Weather radar observation principles

Here we use Figure 2 to describe a feature of weather
radar: the mesh used for wide-area observations. Typically,
the observation range for X-band weather radar is a radiusof approximately 80 km, usually operating with resolution of
150 m radially, yielding data from 533 partitions for distance.
Direction is divided into 1.2° intervals, yielding 300 partitions
over the full 360°. As such, typical X-band weather radar provides
measurements at approximately 160,000 points in real time.
Characteristics of the data are somewhat different, but in simple
terms, a single weather radar installation gives effectively the same
results as 160,000 rain gauges deployed over the area.
■ Figure 2: Typical observation mesh for X-band weatherradar3. Development of weather radar technology
3.1

History of weather radar technology
When weather radar was first introduced, large amounts ofquantitative observations were not immediately available. The
history of the development of weather radar technology is shownin Figure 3.
Initial weather radar sets were analog devices that used aself-excited oscillating electron tube called a magnetron as thetransmitter, projected the output signal from the receiver on a
black-and-white afterglow display, and indicated rain intensity
by brightness. A sketch of this was then made in a darkened
room. Later, with the development of digital technology, systems
became more stable, high-speed processing became possible, andweather radar capable of quantitative precipitation observations
was developed.
In the 1990s, Doppler radar was developed, capable ofobserving both precipitation intensity and air flow (estimatingwind from phase of the reflected signals). From this time, anamplifying device called klystrons came into mainstream use,
enabling stable handling of phase information.
Most types of radar to this point used radio transceivers of
only one polarization (generally horizontal), but at almost thesame time as Doppler radar was developed, dual-polarization
radar, with both horizontal and vertical polarizations, was alsoimplemented. This enabled collection of more information
regarding precipitation particles. Observations made using
both Doppler and dual-polarization are called multi-parameter

observations. Entering the 2000s, radar integrating both Doppler
radar and dual-polarizing radar, or true multi-parameter (MP)radar, became practical.
Around this time Japan took the lead in weather radar
technology around the world. One factor contributing to thiswas the development of solid-state transmitters. This wasachieved by integrating multiple stages of high-output microwave
semiconductors to achieve the transmission power needed forweather radar, successfully transitioning away from the magnetron
and klystron electron tube devices (Figure 4). In 2012, phasedarray weather radar (horizontal polarization type) was developed,
capable of high-speed 3D observations in multiple directions at
the same time, and in 2017, multi-parameter phased array weather
radar with a dual polarization signal function was developed.
■ Figure 4: C-band solid-state multi-parameter weatherradar■ Figure 3: H istory of weather radar technology

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3.2 Multi-parameter observations
Weather radar with a multi-parameter observation function
transmits and receives two linear-polarization signals (horizontaland vertical) (Figure 5). As the precipitation particles becomelarger, they flatten in the horizontal direction due to air resistance(Figure 5). With earlier, non-multi-parameter radar, precipitation
volume was estimated from the received intensity of thehorizontally polarized signal only, so for the same amount of rain,
the volume was over-estimated if the drops were large, and underestimated if the drops were small, as with light rain.
With multi-parameter radar, both intensity and phase for bothhorizontally and vertically polarized signals can be measured, andthese can be combined to derive various parameters.
The “Specific Differential Phase” (KDP) parameter inparticular, has come into broad use to increase the accuracy ofquantitative precipitation observations. Radio waves propagating
through water travel slower than they do in air. As raindrops getlarger, they tend to flatten horizontally, so when radio waves travel
through rain, the phase of horizontally polarized signals tend to bedelayed relative to vertically polarized signals. Taking the ratio of
this phase delay to the rain quantity, regardless of raindrop size, isthe principle behind estimation of rain volume using KDP.
Other parameters can also be derived at the same time,
including polarization parameters: inter-polarization correlation
coefficient (ρHV) and differential radar ref lectivity (ZDR);
and parameters similar to regular Doppler weather radar: theradar reflectivity factor (Zh), Doppler velocity (V), and Doppler
velocity width (W). These parameters help increase the accuracyof precipitation quantity, but are also used in analysis such as
particle classification (whether particles are rain, snow, hail, etc.)
and determining cloud type (stratification, convectional).

4. M
 ulti-parameter phased array weather
radar

Under the direction of a Cabinet Office SIP＊1, MultiParameter Phased Array Weather Radar (MP-PAWR＊2) has
been developed (Figure 6), realizing high-density, highly-accurate,and high speed observations. This radar is installed at Saitama
University, and is currently undergoing verification tests for realoperation.
■ Figure 6: Multi-parameter phased array weather radar
equipment (left) and observation range (right)

We now describe the features of MP-PAWR, touching on
the differences between MP-PAWR and conventional parabolicantenna radar. For 3D observations with conventional radar,plan position indicator (PPI) scans are performed, graduallychanging the angle of elevation for radar observations. This cantake from five to ten minutes to complete. There are also areasspatially between consecutive scans where observations cannot bemade. On the other hand, with MP-PAWR, the scan of antenna
direction is driven mechanically as with conventional radar, but■ Figure 5: Dual-polarization radar observation mechanisms＊1 SIP: Cross-ministerial strategic innovation promotion program＊2 MP-PAWR: Multi-Parameter Phased-Array Weather Radar

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scanning in the vertical direction (altitude) is done electronically.
This enables rapid and dense observations of 3D space up to an
altitude of approximately 15 km, and requires only 30 seconds toone minute to complete (Figure 7).
Figure 8 shows the results from observations over a 30 sinterval, a radius of 60 km and at altitudes up to 15 km. With
MP-PAWR, this sort of 3D observation of rainfall distribution
can be made every 30 seconds to one minute.
Figure 9 shows how features of MP-PAWR can be used.
Various stages of the rapid increase in river water levels after
a sudden heavy downpour are represented conceptually as“Raincloud appearance,” “Rapid raincloud growth,” “Rainfall,”and “Water level rise.” MP-PAWR is able to observe rainfall
amounts in the air at high speed, so during the “rapid raincloudgrowth” stage before the rain reaches the ground, rainfall canbe detected at dense spacing, before it reaches the ground. By
computing rainfall in the air based on MP-PAWR observation
data detected at this stage, using it as input to predict rainfall
amounts, and then to predict water levels, it should be possible
in principle to provide this information earlier than withconventional methods.
This approach is comparable to that of emergency earthquakebulletins. For emergency earthquake bulletins, earthquakes aredetected, having already occurred at the epicenter, and bulletinsare distributed to areas predicted to receive tremors several seconds
before they arrive. This information is used to control a range
of infrastructure automatically, such as pushing notifications
to mobile phones, reducing speed on bullet trains to prevent
derailments, and stopping elevators at the nearest floor to prevent
people getting trapped.

■ Figure 7: C omparison of conventional radar andMP-PAWR antenna scanning methods■ Figure 8: P recipitation observations from MP-PAWR(Aug. 27, 2018)■ Figure 9: Rainfall and water-level prediction with MP-PAWR

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■ Figure 10: U
 tilization of MP-PAWR for emergency heavy-rainfall bulletins

Similarly, emergency heavy-rain bulletins through MPPAWR could be used to detect and notify of heavy rain that has
already occurred in the air, minutes before it reaches the ground.
Specific examples are shown in Figure 10, including disasterprevention measures such as automatically starting drainage
pumps and stopping entry to roadway underpasses, but also
convenience measures such as predicting congestion for car
navigation systems.

Cover Art

5. Conclusion
Japan experiences many damage-causing natural disasters
including earthquakes, volcanoes, heavy rain and gusty wind, andsudden heavy rain is one such phenomenon that causes damage
in society. Reports of conditions made using weather radar anddistributed nationally have long been helpful in reducing damage
from sudden heavy rain storms.
In the future, we hope to contribute to realizing a safer, moresecure society with automatically controlled infrastructure by
combining MP-PAWR 3D observations of precipitation before it
even reaches the ground, with other technologies such as AI andIoT.

Evening at Takanawa (Takanawa noyukei)
Utagawa Hiroshige (1797~1858)
Collection of the Art Research Center (ARC)
Ritsumeikan University
Object number: BN03828992-1-05¯14

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